hi everybody welcome back to re invent 2022. this is thecube's exclusive coverage we're here at the satellite set it's up on the fifth floor of the Venetian Conference Center and this is part of the global startup program the AWS startup showcase series that we've been running all through last year and and into this year with AWS and featuring some of its its Global Partners Ed wallson series the CEO of chaos search many times Cube Alum and Kevin Miller there's also a cube Alum vice president GM of S3 at AWS guys good to see you again yeah great to see you Dave hi Kevin this is we call this our Super Bowl so this must be like your I don't know uh World Cup it's a pretty big event yeah it's the World Cup for sure yeah so a lot of S3 talk you know I mean that's what got us all started in 2006 so absolutely what's new in S3 yeah it's been a great show we've had a number of really interesting launches over the last few weeks and a few at the show as well so you know we've been really focused on helping customers that are running Mass scale data Lakes including you know whether it's structured or unstructured data we actually announced just a few just an hour ago I think it was a new capability to give customers cross-account access points for sharing data securely with other parts of the organization and that's something that we'd heard from customers is as they are growing and have more data sets and they're looking to to get more out of their data they are increasingly looking to enable multiple teams across their businesses to access those data sets securely and that's what we provide with cross-count access points we also launched yesterday our multi-region access point failover capabilities and so again this is where customers have data sets and they're using multiple regions for certain critical workloads they're now able to to use that to fail to control the failover between different regions in AWS and then one other launch I would just highlight is some improvements we made to storage lens which is our really a very novel and you need capability to help customers really understand what storage they have where who's accessing it when it's being accessed and we added a bunch of new metrics storage lens has been pretty exciting for a lot of customers in fact we looked at the data and saw that customers who have adopted storage lens typically within six months they saved more than six times what they had invested in turning storage lens on and certainly in this environment right now we have a lot of customers who are it's pretty top of mind they're looking for ways to optimize their their costs in the cloud and take some of those savings and be able to reinvest them in new innovation so pretty exciting with the storage lens launch I think what's interesting about S3 is that you know pre-cloud Object Store was this kind of a niche right and then of course you guys announced you know S3 in 2006 as I said and okay great you know cheap and deep storage simple get put now the conversations about how to enable value from from data absolutely analytics and it's just a whole new world and Ed you've talked many times I love the term yeah we built chaos search on the on the shoulders of giants right and so the under underlying that is S3 but the value that you can build on top of that has been key and I don't think we've talked about his shoulders and Giants but we've talked about how we literally you know we have a big Vision right so hard to kind of solve the challenge to analytics at scale we really focus on the you know the you know Big Data coming environment get analytics so we talk about the on the shoulders Giants obviously Isaac Newton's you know metaphor of I learned from everything before and we layer on top so really when you talk about all the things come from S3 like I just smile because like we picked it up naturally we went all in an S3 and this is where I think you're going Dave but everyone is so let's just cut the chase like so any of the data platforms you're using S3 is what you're building but we did it a little bit differently so at first people using a cold storage like you said and then they ETL it up into a different platforms for analytics of different sorts now people are using it closer they're doing caching layers and cashing out and they're that's where but that's where the attributes of a scale or reliability are what we did is we actually make S3 a database so literally we have no persistence outside that three and that kind of comes in so it's working really well with clients because most of the thing is we pick up all these attributes of scale reliability and it shows up in the clients environments and so when you launch all these new scalable things we just see it like our clients constantly comment like one of our biggest customers fintech in uh Europe they go to Black Friday again black Friday's not one days and they lose scale from what is it 58 terabytes a day and they're going up to 187 terabytes a day and we don't Flinch they say how do you do that well we built our platform on S3 as long as you can stream it to S3 so they're saying I can't overrun S3 and it's a natural play so it's it's really nice that but we take out those attributes but same thing that's why we're able to you know help clients get you know really you know Equifax is a good example maybe they're able to consolidate 12 their divisions on one platform we couldn't have done that without the scale and the performance of what you can get S3 but also they saved 90 I'm able to do that but that's really because the only persistence is S3 and what you guys are delivering but and then we really for focus on shoulders Giants we're doing on top of that innovating on top of your platforms and bringing that out so things like you know we have a unique data representation that makes it easy to ingest this data because it's kind of coming at you four v's of big data we allow you to do that make it performant on s3h so now you're doing hot analytics on S3 as if it's just a native database in memory but there's no memory SSC caching and then multi-model once you get it there don't move it leverage it in place so you know elasticsearch access you know Cabana grafana access or SQL access with your tools so we're seeing that constantly but we always talk about on the shoulders of giants but even this week I get comments from our customers like how did you do that and most of it is because we built on top of what you guys provided so it's really working out pretty well and you know we talk a lot about digital transformation of course we had the pleasure sitting down with Adam solipski prior John Furrier flew to Seattle sits down his annual one-on-one with the AWS CEO which is kind of cool yeah it was it's good it's like study for the test you know and uh and so but but one of the interesting things he said was you know we're one of our challenges going forward is is how do we go Beyond digital transformation into business transformation like okay well that's that's interesting I was talking to a customer today AWS customer and obviously others because they're 100 year old company and they're basically their business was they call them like the Uber for for servicing appliances when your Appliance breaks you got to get a person to serve it a service if it's out of warranty you know these guys do that so they got to basically have a you know a network of technicians yeah and they gotta deal with the customers no phone right so they had a completely you know that was a business transformation right they're becoming you know everybody says they're coming a software company but they're building it of course yeah right on the cloud so wonder if you guys could each talk about what's what you're seeing in terms of changing not only in the sort of I.T and the digital transformation but also the business transformation yeah I know I I 100 agree that I think business transformation is probably that one of the top themes I'm hearing from customers of all sizes right now even in this environment I think customers are looking for what can I do to drive top line or you know improve bottom line or just improve my customer experience and really you know sort of have that effect where I'm helping customers get more done and you know it is it is very tricky because to do that successfully the customers that are doing that successfully I think are really getting into the lines of businesses and figuring out you know it's probably a different skill set possibly a different culture different norms and practices and process and so it's it's a lot more than just a like you said a lot more than just the technology involved but when it you know we sort of liquidate it down into the data that's where absolutely we see that as a critical function for lines of businesses to become more comfortable first off knowing what data sets they have what data they they could access but possibly aren't today and then starting to tap into those data sources and then as as that progresses figuring out how to share and collaborate with data sets across a company to you know to correlate across those data sets and and drive more insights and then as all that's being done of course it's important to measure the results and be able to really see is this what what effect is this having and proving that effect and certainly I've seen plenty of customers be able to show you know this is a percentage increase in top or bottom line and uh so that pattern is playing out a lot and actually a lot of how we think about where we're going with S3 is related to how do we make it easier for customers to to do everything that I just described to have to understand what data they have to make it accessible and you know it's great to have such a great ecosystem of partners that are then building on top of that and innovating to help customers connect really directly with the businesses that they're running and driving those insights well and customers are hours today one of the things I loved that Adam said he said where Amazon is strategically very very patient but tactically we're really impatient and the customers out there like how are you going to help me increase Revenue how are you going to help me cut costs you know we were talking about how off off camera how you know software can actually help do that yeah it's deflationary I love the quote right so software's deflationary as costs come up how do you go drive it also free up the team and you nail it it's like okay everyone wants to save money but they're not putting off these projects in fact the digital transformation or the business it's actually moving forward but they're getting a little bit bigger but everyone's looking for creative ways to look at their architecture and it becomes larger larger we talked about a couple of those examples but like even like uh things like observability they want to give this tool set this data to all the developers all their sres same data to all the security team and then to do that they need to find a way an architect should do that scale and save money simultaneously so we see constantly people who are pairing us up with some of these larger firms like uh or like keep your data dog keep your Splunk use us to reduce the cost that one and one is actually cheaper than what you have but then they use it either to save money we're saving 50 to 80 hard dollars but more importantly to free up your team from the toil and then they they turn around and make that budget neutral and then allowed to get the same tools to more people across the org because they're sometimes constrained of getting the access to everyone explain that a little bit more let's say I got a Splunk or data dog I'm sifting through you know logs how exactly do you help so it's pretty simple I'll use dad dog example so let's say using data dog preservability so it's just your developers your sres managing environments all these platforms are really good at being a monitoring alerting type of tool what they're not necessarily great at is keeping the data for longer periods like the log data the bigger data that's where we're strong what you see is like a data dog let's say you're using it for a minister for to keep 30 days of logs which is not enough like let's say you're running environment you're finding that performance issue you kind of want to look to last quarter in last month in or maybe last Black Friday so 30 days is not enough but will charge you two eighty two dollars and eighty cents a gigabyte don't focus on just 280 and then if you just turn the knob and keep seven days but keep two years of data on us which is on S3 it goes down to 22 cents plus our list price of 80 cents goes to a dollar two compared to 280. so here's the thing what they're able to do is just turn a knob get more data we do an integration so you can go right from data dog or grafana directly into our platform so the user doesn't see it but they save money A lot of times they don't just save the money now they use that to go fund and get data dog to a lot more people make sense so it's a creativity they're looking at it and they're looking at tools we see the same thing with a grafana if you look at the whole grafana play which is hey you can't put it in one place but put Prometheus for metrics or traces we fit well with logs but they're using that to bring down their costs because a lot of this data just really bogs down these applications the alerting monitoring are good at small data they're not good at the big data which is what we're really good at and then the one and one is actually less than you paid for the one so it and it works pretty well so things are really unpredictable right now in the economy you know during the pandemic we've sort of lockdown and then the stock market went crazy we're like okay it's going to end it's going to end and then it looked like it was going to end and then it you know but last year it reinvented just just in that sweet spot before Omicron so we we tucked it in which which was awesome right it was a great great event we really really missed one physical reinvent you know which was very rare so that's cool but I've called it the slingshot economy it feels like you know you're driving down the highway and you got to hit the brakes and then all of a sudden you're going okay we're through it Oh no you're gonna hit the brakes again yeah so it's very very hard to predict and I was listening to jassy this morning he was talking about yeah consumers they're still spending but what they're doing is they're they're shopping for more features they might be you know buying a TV that's less expensive you know more value for the money so okay so hopefully the consumer spending will get us out of this but you don't really know you know and I don't yeah you know we don't seem to have the algorithms we've never been through something like this before so what are you guys seeing in terms of customer Behavior given that uncertainty well one thing I would highlight that I think particularly going back to what we were just talking about as far as business and digital transformation I think some customers are still appreciating the fact that where you know yesterday you may have had to to buy some Capital put out some capital and commit to something for a large upfront expenditure is that you know today the value of being able to experiment and scale up and then most importantly scale down and dynamically based on is the experiment working out am I seeing real value from it and doing that on a time scale of a day or a week or a few months that is so important right now because again it gets to I am looking for a ways to innovate and to drive Top Line growth but I I can't commit to a multi-year sort of uh set of costs to to do that so and I think plenty of customers are finding that even a few months of experimentation gives them some really valuable insight as far as is this going to be successful or not and so I think that again just of course with S3 and storage from day one we've been elastic pay for what you use if you're not using the storage you don't get charged for it and I think that particularly right now having the applications and the rest of the ecosystem around the storage and the data be able to scale up and scale down is is just ever more important and when people see that like typically they're looking to do more with it so if they find you usually find these little Department projects but they see a way to actually move faster and save money I think it is a mix of those two they're looking to expand it which can be a nightmare for sales Cycles because they take longer but people are looking well why don't you leverage this and go across division so we do see people trying to leverage it because they're still I don't think digital transformation is slowing down but a lot more to be honest a lot more approvals at this point for everything it is you know Adam and another great quote in his in his keynote he said if you want to save money the Cloud's a place to do it absolutely and I read an article recently and I was looking through and I said this is the first time you know AWS has ever seen a downturn because the cloud was too early back then I'm like you weren't paying attention in 2008 because that was the first major inflection point for cloud adoption where CFO said okay stop the capex we're going to Opex and you saw the cloud take off and then 2010 started this you know amazing cycle that we really haven't seen anything like it where they were doubling down in Investments and they were real hardcore investment it wasn't like 1998 99 was all just going out the door for no clear reason yeah so that Foundation is now in place and I think it makes a lot of sense and it could be here for for a while where people are saying Hey I want to optimize and I'm going to do that on the cloud yeah no I mean I've obviously I certainly agree with Adam's quote I think really that's been in aws's DNA from from day one right is that ability to scale costs with with the actual consumption and paying for what you use and I think that you know certainly moments like now are ones that can really motivate change in an organization in a way that might not have been as palatable when it just it didn't feel like it was as necessary yeah all right we got to go give you a last word uh I think it's been a great event I love all your announcements I think this is wonderful uh it's been a great show I love uh in fact how many people are here at reinvent north of 50 000. yeah I mean I feel like it was it's as big if not bigger than 2019. people have said ah 2019 was a record when you count out all the professors I don't know it feels it feels as big if not bigger so there's great energy yeah it's quite amazing and uh and we're thrilled to be part of it guys thanks for coming on thecube again really appreciate it face to face all right thank you for watching this is Dave vellante for the cube your leader in Enterprise and emerging Tech coverage we'll be right back foreign

(upbeat music) >> Woman Voiceover: From our studios in the heart of Silicon Valley, Palo Alto, California this is a CUBE Conversation. >> Hello and welcome to this special CUBE Conversation here in Palo Alto, California, CUBE Studios, I'm John Furrier, host of theCUBE, here with Sri Ambati. He's the founder and CEO of H20.ai. CUBE Alum, hot start up right in the action of all the machine learning, artificial intelligence, with democratization the role of data in the future, it's all happening with Cloud 2.0, DevOps 2.0, Sri, great to see you. Thanks for coming by. You're a neighbor, you're right down the street from us at our studio here. >> It's exciting to be at theCUBE Com. >> That's KubeCon, that's Kubernetes Con. CUBEcon, coming soon, not to be confused with KubeCon. Great to see you. So tell us about the company, what's going on, you guys are smoking hot, congratulations. You got the right formula here with AI. Explain what's going on. >> It started about seven years ago, and .ai was just a new fad that arrived that arrived in Silicon Valley. And today we have thousands of companies in AI, and we're very excited to be partners in making more companies become AI-first. And our vision here is to democratize AI, and we've made it simple with our open source, made it easy for people to start adapting data science and machine learning in different functions inside their large organizations. And apply that for different use cases across financial services, insurance, health care. We leapfrogged in 2016 and built our first closed source product, Driverless AI, we made it on GPUs using the latest hardware and software innovations. Open source AI has funded the rise of automatic machine learning, Which further reduces the need for extraordinary talent to fill the machine learning. No one has time today, and then we're trying to really bring that automatic machine learning at a very significant crunch time for AI, so people can consume AI better. >> You know, this is one of the things that I love about the current state of the market right now, the entrepreneur market as well as startups and growing companies that are going to go public. Is that there's a new breed of entrepreneurship going on around large scale, standing up infrastructure, shortening the time it takes to do something. Like provisioning. The old AIs, you got to be a PHD. And we're seeing this in data science, you don't have to be a python coder. This democratization is not just a tag line, actually the reality is of a business opportunity. Whoever can provide the infrastructure and the systems for people to do it. It is an opportunity, you guys are doing that. This is a real dynamic. This is a new way, a new kind of dynamic and an industry. >> The three real characteristics on ability to adopt AI, one is data is a team sport. Which means you've got to bring different dimensions within your organization to be able to take advantage of data and AI. And you've got to bring in your domain scientists, work closely with your data scientists, work closely with your data engineers, produce applications that can be deployed, and then get your design on top of it that can convince users or strategists to make those decisions that data is showing up So that takes a multi-dimensional workforce to work closely together. The real problem in adoption of AI today is not just technology, it's also culture. So we're kind of bringing those aspects together in formal products. One of our products, for example, Explainable AI. It's helping the data scientists tell a story that businesses can understand. Why is the model deciding I need to take this test in this direction? Why is this model giving this particular nurse a high credit score even though she doesn't have a high school graduation? That kind of figuring out those democratization goes all the way down. Why is the model deciding what it's deciding, and explaining and breaking that down into English. And building a trust is a huge aspect in AI right now. >> Well I want to get to the talent, and the time, and the trust equation on the next talk, but I want to get the hard news out there. You guys have some news, Driverless AI is one of your core things. Explain the news, what's the big news? >> The big news has been that... AI's a money ball for business, right? And money ball as it has been played out has been the experts were left out of the field, and algorithms taking over. And there is no participation between experts, the domain scientists, and the data scientists. And what we're bringing with the new product in Driverless AI, is an ability for companies to take our AI and become AI companies themselves. The real AI race is not between the Googles and the Amazons and the Microsofts and other AI companies, AI software companies. The real AI race is in the verticals and how can a company which is a bank, or an insurance giant, or a healthcare company take AI platforms and become, take the data and monetize the data and become AI companies themselves. >> Yeah, that's a really profound statement I would agree with 100% on that. I think we saw that early on in the big data world around Hadoop, well Hadoop kind of died by the wayside, but Dave Vellante and the WikiBon team have observed, and they actually predicted, that the most value was going to come from practitioners, not the vendors. 'Cause they're the ones who have the data. And you mentioned verticals, this is another interesting point I want to get more explanation from you on, is that apps are driven by data. Data needs domain-specific information. So you can't just say "I have data, therefore magic happens" it's really at the edge of the domain speak or the domain feature of the application. This is where the data is, so this kind of supports your idea that the AI's about the companies that are using it, not the suppliers of the technology. >> Our vision has always been how we make our customers satisfied. We focus on the customer, and through that we actually make customer one of the product managers inside the company. And the doors that open from working very closely with some of our leading customers is that we need to get them to participate and take AIs, algorithms, and platforms, that can tune automatically the algorithms, and have the right hyper parameter optimizations, the right features. And augment the right data sets that they have. There's a whole data lake around there, around data architecture today. Which data sets am I not using in my current problem I'm solving, that's a reasonable problem I'm looking at. That combination of these various pieces have been automated in Driverless AI. And the new version that we're now bringing to market is able to allow them to create their own recipes, bring their own transformers, and make an automatic fit for their particular race. So if you think about this as we built all the components of a race car, you're going to take it and apply it for that particular race to win. >> John: So that's the word driverless comes in. It's driverless in the sense of you don't really need a full operator, it kind of operates on its own. >> In some sense it's driverless. They're taking the data scientists, giving them a power tool. Historically, before automatic machine learning, driverless is in the umbrella of machine learning, they would fine tune, learning the nuances of the data, and the problem at hand, what they're optimizing for, and the right tweaks in the algorithm. So they have to understand how deep the streets are going to be, how many layers of deep learning they need, what variation of deep learning they should put, and in a natural language crossing, what context they need. Long term shot, memory, all these pieces they have to learn themselves. And there were only a few grand masters or big data scientists in the world who could come up with the right answer for different problems. >> So you're spreading the love of AI around. >> Simplifying that. >> You get the big brains to work on it, and democratization means people can participate and the machines also can learn. Both humans and machines. >> Between our open source and the very maker-centric culture, we've been able to attract some of the world's top data scientists, physicists, and compiler engineers. To bring in a form factor that businesses can use. One data scientist in a company like Franklin Templeton can operate at a level of ten or hundreds of them, and then bring the best in data science in a form factor that they can plug in and play. >> I was having a concert with Kent Libby, who works with me on our platform team. We have all this data with theCUBE, and we were just talking, we need to hire a data scientist and AI specialist. And you go out and look around, you've got Google, Amazon, all these big players spending between 3-4 million per machine learning engineer. And that might be someone under the age of 30 with no experience. So the talent bore is huge. The cost to just hire, we can't hire these people. >> It's a global war. There's talent shortage in China, there's talent shortage in India, there's talent shortage in Europe, and we have offices in Europe and India. There's a talent shortage in Toronto and Ottawa. So it's a global shortage of physicists and mathematicians and data scientists. So that's where our tools can help. And we see Driverless AI as, you can drive to New York or you can fly to New York. >> I was talking to my son the other day, he's taking computer science classes in night school. And it's like, well you know, the machine learning in AI is kind of like dog training. You have dog training, you train the dog to do some tricks, it does some tricks. Well, if you're a coder you want to train the machine. This is the machine training. This is data science, is what AI possibility is there. Machines have to be taught something. There's a base input, machines just aren't self-learning on their own. So as you look at the science of AI, this becomes the question on the talent gap. Can the talent gap be closed by machines? And you got the time, you want speed, low latency, and trust. All these things are hard to do. All three, balancing all three is extremely difficult. What's your thoughts on those three variables? >> So that's why we brought AI to help with AI. Driverless AI is a concept of bringing AI to simplify. It's an expert system to do AI better. So you can actually give to the hands of the new data scientists, so you can perform at the power of an advanced data scientist. We're not disempowering the data scientist, the part's still for a data scientist. When you start with a confusion matrix, false positives, false negatives, that's something a data scientist can understand. When you talk about feature engineering, that's something a data scientist can understand. And what Driverless AI is really doing is helping him do that rapidly, and automated on the latest hardware, that's where the time is coming into. GPUs, FPGAs, TPUs, different form of clouds. Cheaper, right. So faster, cheaper, easier, that's the democratization aspect. But it's really targeted at the data scientist to prevent experimental error. In science, the data science is a search for truth, but it's a lot of experiments to get to truth. If you can make the cost of experiments really simple, cheaper, and prevent over fitting. That's a common problem in our science. Prevent bias, accidental bias that you introduce because the data is biased, right. So trying to prevent the flaws in doing data science. Leakage, usually your signal leaks, and how do you prevent those common pieces. That's where Driverless AI is coming at it. But if you put that in a box, what that really unlocks is imagination. The real hard problems in the world are still the same. >> AI for creative people, for instance. They want infrastructure, they don't want to have to be an expert. They want that value. That's the consumerization. >> AI is really the co founder for someone who's highly imaginative and has courage, right. And you don't have to look for founders to look for courage and imagination. A lot of entrepreneurs in large companies, who are trying to bring change to their organizations. >> Yeah, we always say, the intellectual property game is changing from protocols, locked in, patented, to you could have a workflow innovation. Change one little tweak of a process with data and powerful AI, that's the new magic IP equation. It's in the workflow, it's in the application, it's new opportunities. Do you agree with that? >> Absolutely. The leapfrog from here is businesses will come up with new business processes. So we looked at business process optimization, and globalization's going to help there. But AI, as you rightfully said earlier, is training computers. Not just programming them, you're schooling them. A host of computers that can now, with data, think almost at the same level as a Go player. The world's leading Go player. They can think at the same level of an expert in that space. And if that's happening, now I can transform. My business can run 24 by 7 and the rate at which I can assemble machines and feed it data. Data creation becomes, making new data becomes, the real value that AI can- >> H20.ai announcing Driverless AI, part of their flagship product around recipes and democratizing AI. Congratulations. Final point, take a minute to explain to the folks just the product, how they buy it, what's it made of, what's the commitment, how do they engage with you guys? >> It's an annual license, a software license people can download on our website. Get a three week trial, try it on their own. >> Free trial? >> A free trial, our recipes are open-source. About a hundred recipes, built by grand masters have been made open source. And they can be plugged, and tried. Customers of course don't have to make their software open source. They can take this, make it theirs. And our vision here is to make every company an AI company. And that means that they have to embrace AI, learn it, tweak it, participate, some of the leading conservation companies are giving it back in the open source. But the real vision here is to build that community of AI practitioners inside large organizations. We are here, our teams are global, and we're here to support that transformation of some large customers. >> So my problem of hiring an AI person, you could help me solve that. >> Right today. >> Okay, so anyone who's watching, please get their stuff and come get an opening here. That's the goal. But that is the dream, we want AI in our system. >> I have watched you the last ten years, you've been an entrepreneur with a fierce passion, you want AI to be a partner so you can take your message to wider audience and build monetization around the data you have created. Businesses are the largest, after the big data warlords we have, and data privacy's going to come eventually, but I think businesses are the second largest owners of data they just don't know how to monetize it, unlock value from it, and AI will help. >> Well you know we love data, we want to be data-driven, we want to go faster. Love the driverless vision, Driverless AI, H20.ai. Here in theCUBE I'm John Furrier with breaking news here in Silicon Valley from hot startup H20.ai. Thanks for watching.

>> Narrator: Live from Las Vegas, it's theCUBE! Covering IBM Think 2018. Brought to you by IBM. >> Welcome back to IBM Think 2018, you're watching theCUBE, the leader in live tech coverage, my name is Dave Vellante, I'm here with my co-host Peter Burris. Gene Chao is here as the Global VP of IBM Automation and Jason Kelley, Cube Alum, is the GM of Blockchain Services. Gentlemen, welcome back to theCUBE. >> Thank you much. >> Great to see you. >> You guys, I call you heat-seeking inefficiency missiles, so, Jason's... Just a shout-out, take it from there. What are you guys up to, what are you doing? How are you helping businesses? >> Well, we're driving trust into transactions. The elusive things that we've been trying to-- >> Gene: Whoops, there goes heat-seeking. (laughing) >> Exactly. Or we're seeking the heat. It's coming after us, as soon as we say trust, someone wants to attack you. And so what we're bringing into business is that thought that, if I can add trust into transactions, I don't need a third-party to validate it. I can now say, look, you are who you are. We both know each other. All that we do, we go way back. We know each other, and what we're about to exchange is known as well. So if I can keep that validation from happening, I'm going to remove cost, labor, time, out of it. And I'm also going to then maybe avail new market opportunities of those who could not enter the system before because we didn't trust their identities. Or we didn't trust that their goods were their goods, and they were trying to exchange it. So think of that heat-seeking missile, we're trying to bring that capability and that heat is the energy in the system now going bigger, better, faster because there's trust. >> And your role is to bring those Blockchain services to market, is that right? >> That's correct, bringing the services as a whole, because see, Blockchain isn't a product. Blockchain, you know, I don't have under the table a bucket of Blockchain. >> Dave: Let me see your Blockchain. >> Sorry, no Blockchains here. So, if in fact, we're bringing this capability to the market, there's all types of services from what's the business value design? First, what's your outcome? Why say Blockchain? Believe it or not, it says it on my chest, so it means I get paid to do it, but maybe you don't need this? And so, quite simply, maybe you need to do something else. So the first thing is, let's understand the outcome that your business is running toward, and then let's understand if it's a Blockchain, and then can we bring some automation with Gene and team? >> Okay, that's the set-up for you Gene, so you're the automation piece of the puzzle. Explain. >> So, I love the commentary around the better, faster, but we're also bringing more scale. So automation has scale. What does that mean? We're really focused on two things, guys, the first thing is around taking advantage of the new technologies to enable what I'll call software-based labor. So there's a new concept of the digital workforce model that enables how transactions or how work gets done. Coupled with that is how that workflow or process, business process, IT process, whatever it is, how does that workflow fundamentally change through these technologies. Why that's important is as we look at Blockchain, as an example, as a pivot point for trusted transactions, I need to build trusted automation around it. Trusted ways to leverage these technologies in that workflow so those transactions are easily scalable, works at machine time, and runs through very quickly. >> This is fascinating stuff, 'cause look. The way that we like to characterize the big change in the industry is we say, for the first 50 years of computing, there was no process, accounting, HR, et cetera, on known technology. How do we implement? What technology do you choose to implement? The implementation choices are becoming clear. Cloud, et cetera. What's less known is the process. The unknown process, unknown technology. Now it's unknown process, known technology. And what you guys are talking about is one of the challenges when you think about processes. Who does what? Can we verify that we've done it? Did they do it right? Did they meet to do what they said they were doing? Et cetera, the whole range of issues. And the contracting process is extremely complex, but if you set it up in a Blockchain form, you've got a simple contract, a simple definition of who is trusted, simple definitions of roles, and now we can dramatically accelerate new process creation and then automate it. Have I got that right? >> I think you got it, when you think about dramatically, dramatically accelerated, you say that it means something different to everyone. But let's think about my friend Frank Yiannas at Wal-Mart, for example, where they're working on food trust. They're trying to make sure that from farm to fork, we know where that food came from. One-third of all food that's processed goes to waste. Because we lack food trust. Food is guilty until proven innocent, right? To keep that from being-- >> Spoiled. >> Spoiled, I'm... The humor is killing me. (laughing) So, no pun intended, food trust, right? So, Frank and team wanted to understand how fast they could move this thought of tracking, tracing, with transparency, this food through the system. Just as you said, there's certain contrast, think of the handshakes from getting, in their case, a mango from a farm all the way to your home, Well, it used to take them seven days. Actually, six days, twenty-some hours, in order to figure out that process. Put it on the Blockchain? 12 seconds. And then once they cured the lag and the technology, 2.2 seconds. So think of that. Now you're shrinking this to seconds versus days, what does that do to the process? What do you do when you say, now my system can go that fast. My people can go that fast. What do you do? Think of the automation that you're bringing in now, and things that you will now have to automate, out of not just necessity, but things you will say, wow, we've opened up a whole new ecosystem of possibilities in order to do business in a different way. >> Well, so let me build on that for a second. 'Cause one of the things that potentially means is that because you can handle more complex, newly designed, process, better, faster, more automated, that you can start to expand the scope of participants in a transaction? The range of characteristics of the transaction, or the type of work? That's how you build up to new businesses and new business models, right? >> Sure. >> Right, right. >> If I can jump in on that one. There's a concept in this one, and this is where Jason and I are connected at the hip. You know, we think in terms of a smarter product, we think in terms of a smarter contract, or transaction, that the guiding principle that we're using is the old way of thinking, and I carry this narrative all over with me is, the old way of thinking is you have people following your creating process, supported by that technology. So the things that you talked about, unknown technology, unknown process, continuously sourced by people? Fundamentally changed. We're now working in a world where the process is run by the technology and supported by the people. It's not that the people are going away, it's a fundamental retooling of the skills and understanding of how to support it, but that scalability, the ability to get to that exponential growth, is because the process is the king. At the top of the food chain, now. And that technology lets it expand. >> But we could do levels of complexity in that process and the number of participants in that process, unheard of! It's scale and scope. >> Yes. >> But doesn't that force... Look, we've had some conversations, Dave and I have had some conversations, with a number of big user organizations about this stuff and we keep coming back to the issue of that they can't just look at the technology, they have to focus on the design. That one of the most crucial features of this process is the design of the Blockchain. We got that right? >> You heard me use the phrase at the very beginning, if you didn't, I'll say it again, I said, business value design. Because in fact, that design is not just a UI or UX, but let's make sure that the business and technology are doing the right thing to get to the outcome. As we say, design doesn't stop until the problem is solved. And guess what, the problem's never solved. So design happens... Many people say, "Oh we're going to do some "design thinking at the beginning. "We did that," check the block, and then they run off and do something else. For us, design's like an infinity loop. You continue to do it. From the beginning all the way to the end, and then, what you're able to do, and hint-hint, this is something that we do in our services, we start with our clients, we get them started so they understand, then we help them accelerate, and then innovate. Three steps: start, accelerate, innovate. And that's a design process in and of itself. So if you start at, you know, the days of Blockchain tourism were a couple years ago, everybody wanted to kick the tires, and then last year was PoC PoV, this year's the year of production. And people are quick in saying, "How do I quickly start "production and keep moving?" >> So let's talk about some other examples. You mentioned Wal-Mart, we heard Plastic Mag this morning, I introduced somebody, I think Evercorp was the name of the company, Diamond Providence. Others that you're excited about, that have made a business impact. >> Well, I'd be remiss if I didn't mention Mike White and others at our JV with Maersk. And you know, you think of that, where you have the classic thought of a supply chain, this linear steps in the process, you know, these handshakes that have to happen. Now what we have is we have this process of thinking how we can bring transparency into all of that, and it's not just a supply chain, but a value chain. So you have where 80% of whatever you all are touching or have owned right now, with the shipping line. But not only through a shipping line, but then there was also ground and air, and ultimately to a retail location. Then you consumed it. Well, think of all of those processes now having the transparency where you can see from point of consumption all the way back to origin. Think of the supply chain visibility, that elusive thing called supply chain optimization. Now you can do that, but not only the supply chain, but the value chain. Someone's paying invoices under that big thing called a value chain. Someone's doing trade promotion management in that value chain. Now, if you have that visibility, what do you enable? How many more packages can go through the system? How much more shipping? And the estimate is 5% increase in GDP if we're able to get all of this shipping into the Blockchain. You start talking GDP? It opens eyes. >> Right now you're talking growth, right? >> Yes. >> Real growth. >> So, it's 20% of the four trillion associated with shipping? Is bound up in paperwork? >> Yes. >> So we're talking about 800 billion dollar change. >> And returning capital into the system. Returning capital. You think of this thought of opening up new opportunity, And I'll throw another example, another client, so we're not just talking, but you think of what's happening with We.Trade. Nine banks in Europe who compete. You think of Santander Bank and a Deustche Bank and those are now, they're all coming together, saying "How do we now share data and information "so that we can let small to medium size enterprises "into the system?" So now you're getting not just savings of cost and time, but now you're opening up markets. Getting greater throughput. High waters raise all boats. And that's what we're seeing in a lot of these examples with, it's not just taking out those old things, you're thinking of new processes running the business a different way. >> And Jason's a great lead guy. You asked for an example, our friends at DBS Bank. They are fundamentally looking at changing the business models within the bank across all different divisions of the bank, whether it's credit transactions, mortgages, personal wealth, and the way they approached it was, we know these new technologies are going to allow us to fundamentally look at the workflow and change it. But here's the question: Who will be looking at changing these things? What's going to enable these model changes, the workflow changes may not be human capital. It may be working alongside this sort of man plus machine element or formula-- >> Peter: Patterns. >> Right, to allow the technology to tell you where your efficiencies could be gained. Allow the technologies to make the correlations in those disparate business models, to fundamentally change how you do business. So that's happening today. >> So, phase one is what is this, phase two, POC, now you're sort of in real production, but you obviously doing a lot more POCs, you're scaling out. Where do you see this going over the next three or four years? >> Well, I think last year was a year of the PoC PoV. I think this year's a year of production. And when you think of some of the examples that we've given, we've talked about consumer trade with Wal-Mart, we talk about shipping trade with Maersk, we talk about trade finance with We.Trade. Each of those individual networks, where do we see it going? We see these networks becoming a network of networks. Where each one of them have their own ecosystems and they come together. And they come together with trusted data, with trusted information, access that's unparalleled. So that's where we see it heading. And you have to say then, okay, it sounds really simple in the way you've just described it, so where's the challenge? The challenge is going to be doing this from a business and technology perspective. There's a lot of things that have to be figured out here. How are you going to make those processes work at that speed? What do you rightfully automate and what things don't you automate? That's more than just a technology. You can't plug a technology in and solve this. It takes an end to end capability. And that's what we're seeing, becoming more of a differentiating capability for our teams, where they can say, "Gene, Jason, "can your teams talk to us together?" 'Cause, of course, they work together. That's a differentiating effect of moving at scale and at speed, and that's where we see it going. Scale and speed. >> So what Jason and the Blockchain frame does for us, is it's an accelerant. Okay, we talk about knowledge worker, automation, we talk about different areas of software-based labor, but that accelerant is doing one big thing, is it's forcing us into what I'll call vertically integrated processes or workflow. Gone are the days of segmentation of, "Oh, that's back office," or "That's front office." We now have to take that workflow and pivot that to vertical integration. Why? That accelerant is moving at the speed of light for trusted transactions, I have to make the systems supporting that. The process, the people, I have to keep up with that pace of change. If I don't vertically integrate those processes inter and intracompany? This doesn't work. It falls down. So that's our marriage. >> Tough to go to market. How do you go to market? >> How do we go to market? We go to market as fast as we can, and we go joined at the hip, with clear and simple understanding. >> Where's the Blockchain for going to market? >> Yeah, right? >> And is there partner ecosystem that... >> Absolutely. So we talk about a Blockchain, Blockchain's a team sport. And it is a true demonstration of Metcalfe's Law, you know, the network drives the value. And so we do. We go to market with this thought of, who's going to play in that network? And we have networks where its obvious value may have a founder network, like Wal-Mart, where you say look, we see the ecosystem, we have the ecosystem, we're the founding partner, or you have a consortium such as We.Trade, where they come in and they say, "Look, let's pull all this together "'cause we see the value." So we go to market with that ecosystem, knowing that they have to partner, they have to work together. >> Outstanding. >> There's three distinct chapters in our go to market strategy. One is the services architecture, the second one is software ecosystem, and the third is around platforms, like a Blockchain. So when we start-- >> No design? >> Sorry, say again? >> No design? >> No, there is absolutely design. Absolutely design. So at a service architecture's perspective, there is fundamental workflow design happening. At a platform level, that's an even further advancement of design, because of the frameworks and blueprints happening inside a Blockchain, inside the different next-gen technologies happening. So I have to be two things, I have to be an automation-led environment where I'm providing the way to do these things, differences in RPA versus other technologies, but I also have to be an automation-attached. I have to be attached into the Blockchain framework to make sure we're coupled in the different elements of that framework. So that's how we jointly go to market. >> Peter: RPAs, I'm sorry? >> I'm sorry, Robotic Process Automation companies, so these are the relatively new technologies that enable software-based labor components. They're replicating human activity. >> Software robots? >> Software robots. >> You have a path to automation anyway. >> Exactly right. Exactly right. >> And it's funny when you ask, you know, no design. Design's in there. And this is the way we work at IBM, I mean, we're past that calling it out. So if someone's calling it out, it's like you're going to buy a phone and say, "Oh yeah, we included the battery." Like, it's there now, right? So that's how we run. So is it in there? You mention IBM, anything that you're going to consume from us? Includes IBM design. By practice. >> Wow, you guys, today was Blockchain day. I mean, you must have been thrilled to see all the main tech-- >> You mean every day's not Blockchain day? >> Dave: Well, at IBM, thinks every day... >> Okay, alright, I was just checking. >> You guys sucked all of the air out of the morning. And we heard-- >> And by the way, I certainly hope not. (laughing) >> You hope not what? >> That every day is Blockchain day. >> I hope so. Jason here. >> Makes me not have to buy a new wardrobe. >> If every day's Blockchain day, it ain't working. This is going to be one of those technologies, the less we know about it, the more successful it's been. >> I agree, I agree. >> Well, gentlemen, thanks very much for coming on theCUBE. Always a pleasure. >> Thank you guys. >> Thanks very much. >> Appreciate it. >> Alright, keep it right there, buddy. We'll be back with our next guest right after this short break. You're watching theCUBE live from IBM Think 2018. Be right back.

>> Announcer: Live, from Las Vegas, it's the Cube. Covering InterConnect 2017. Brought to you by, IBM. >> Okay welcome back everyone, we're here live in Las Vegas for IBM InterConnect 2017, this is the Cube coverage here, in Las Vegas for IBM's cloud and data shows. It turns out, I'm John Furrier, with my cohost Dave Vellante, next guess is Jamie Thomas, general manager of systems development and strategy at IBM, Cube Alum. Great to see you, welcome back. >> Thank you, great to see you guys as usual. >> So, huge crowds here. This is I think, the biggest show I've been to for IBM. It's got lines around the corner, just a ton of traffic online, great event. But it's the cloud show, but it's a little bit different. What's the twist here today at InterConnect? >> Well, if you saw the Keynote, I think we've definitely demonstrated that while we're focused on differentiating experience on the cloud through cloud native services, we're also interesting in bridging existing clients IT investments into that environment. So, supporting hybrid cloud scenarios, understanding how we can provide connective fabric solutions, if you will, to enable clients to run mobile applications on the cloud and take advantage of the investments they've made and their existing transactional infrastructure over a period of time. And so the Keynote really featured that combination of capabilities and what we're doing to bring those solution areas to clients and allow them to be productive. >> And the hybrid cloud is front and center, obviously. IOT on the data side, you've seen a lot of traction there. AI and machine learning, kind of powering and lifting this up, it's a systems world now, I mean this is the area that you're in. Cause you have the component pieces, the composibility of that. How are you guys facilitating the hybrid cloud journey for customers? Because now, it's not just all here it is, I might have a little bit of this and a little bit of that, so you have this component-isationer composobility that app developers are consistent with, yet the enterprises want that work load flexibility. What do you guys do to facilitate that? >> Well we absolutely believe that infrastructure innovation is critical on this hybrid cloud journey. And we're really focused on three main areas when we think about that innovation. So, integration, security, and supportive cognitive workloads. When we look at things like integration, we're focused on developers as key stake holders. We have to support the open communities and frameworks that they're leveraging, we have to support API's and allow them to tap into our infrastructure and those investments once again, and we also have to ensure that data and workload can be flexibly moved around in the future because these will allow better characteristics for developers in terms of how they're designing their applications as they move forward with this journey. >> And the insider threat, though, is a big thing too. >> Yes. >> I mean security is not only table stakes, it's a highly sensitive area. >> It's a given. And as you said, it's not just about protecting from the outside threats, it's about protecting from internal threats, even from those who may have privileged access to the systems, so that's why, with our systems infrastructure, we have protected from the chip, all the way through the levels of hardware into the software layer. You heard us talk about some of that today with the shipment of secure service containers that allow us to support the system both at install time and run time, and support the applications and the data appropriately. These systems that run Blockchain, our high security Blockchain services, LinuxONE, we have the highest certification in the industry, EAL five plus, and we're supporting FIPS 120-two, level four cryptology. So it's about protecting at all layers of the system, because our perspective is, there's not a traditional barrier, data is the new perimeter of security. So you've got to protect the data, at rest, in motion, and across the life cycle of the data. >> Let's go back to integration for a second. Give us an example of some of the integrations that you're doing that are high profile. >> Well one of the key integrations is that a lot of clients are creating new mobile applications. They're tapping back into the transactions that reside in the mainframe environment, so we've invested in ZOS Connect and this API set of capabilities to allow clients to do that. It's very prevalent in many different industries, whether it's retail banking, the retail sector, we have a lot of examples of that. It's allowing them to create new services as well. So it's not just about extending the system, but being able to create entirely new solutions. And the areas of credit card services is a good example. Some of the organizations are doing that. And it allows for developer productivity. >> And then, on the security side, where does encryption fit? You mentioned you're doing some stuff at the chip level, end to end encryption. >> Yeah it really, it's at all levels, right? From the chip level, through the firmware levels. Also, we've added encryption capability to ensure that data is encrypted at rest, as well as in motion, and we've done that in a way that encrypts these data sets that are heavily used in the main frame environment as an example, without impending on developer productivity. So that's another key aspect of how we look at this. How can we provide this data protection? But once again, not slow down the velocity of the developers. Cause if we slow down the velocity of the developers, they will be an inhibitor to achieving the end goal. >> How important is the ecosystem on that point? Because you have security, again, end to end, you guys have that fully, you're protecting the data as it moves around, so it's not just in storage, it's everywhere, moving around, in flight, as they say. But now you got ecosystem parties, cause you got API economy, you're dealing with no perimeter, but now also you have relationships as technology partners. >> Yes, well the ecosystem is really important. So if we think about it from a developer perspective, obviously supporting these open frameworks is critical. So supporting Linux and Docker and Spark and all of those things. But also, to be able to innovate at the rate and pace we need, particularly for things like cognitive workloads, that's why we created the Open Power Foundation. So we have more than 300 partners that we're able to innovate with, that allow us to create the solutions that we think we'll need for these cognitive workloads. >> What is a cognitive workload? >> So a cognitive workload is what I would call an extremely data hungry workload, the example that we can all think of is we're expecting, when we experience the world around us, we're expecting services to be brought to us, right, the digital economy understands our desires and wants and reacts immediately. So all of that is driving, that expectation is driving this growth and artificial intelligence, machine learning, deep learning type algorithms. Depending on what industry you're in, they take on a different persona, but there's so many different problems that can be solved by this, whether it's I need to have more insight into the retail offers I provide to an in consumer, to I need to be able to do fraud analytics because I'm in the financial services industry, there's so many examples of these cognitive applications. The key factors are just, tremendous amount of data, and a constrained amount of time to get business insight back to someone. >> When you do these integrations and you talk about the security investments that you're making, how do you balance the resource allocation between say, IBM platforms, mainframe, power, and the OS's, the power in those, and Linux, for example, which is such a mainstay of what you guys are doing. Are you doing those integrations on the open side as well in Linux and going deep into the core, or is it mostly focused on, sort of, IBM owned technology? >> So it really depends on what problem we're trying to solve. So, for instance, if we're trying to solve a problem where we're marrying data insight with a transaction, we're going to implement a lot of that capability on ZOS, cause we want to make sure that we're reducing data latency and how we execute the processing, if you will. If we're looking at things like new work loads and evolution of new work loads, and new things are being created, that's more naturally fit for purpose from a Linux perspective. So we have to use judgment, a lot of the new programming, the new applications, are naturally going to be done on a Linux platform, cause once again that's a platform of choice for the developer community. So, we have to think about whether we're trying to leverage existing transactions with speed, or whether we're allowing developers to create new assets, and that's a key factor in what we look at. >> Jamie, your role, is somewhat unique inside of IBM, the title of GM system's development and strategy. So what's your scope, specifically? >> So, I'm responsible for the systems development involved in our processor's mainframes, power systems, and storage. And of course, as a strategy person for a unit like that, I have responsibility for thinking about these hybrid scenarios and what do we need to do to make our clients successful on this journey? How do we take advantage of their tremendous investments they made with us over years. We have strong responsibility for those investments and making sure the clients get value. And also understanding where they need to go in the future and evolving our architecture and our strategic decisions, along those lines. >> So you influence development? >> Jamie: Yes. >> In a big way, obviously. It's a lot of roadmap work. >> Jamie: Yes. >> A lot of working with clients to figure out requirements? >> Well I have client support too, so I have to make sure things run. >> What about quantum computing? This has been a big topic, what's the road map look like? What's the evolution of that look like? Talk about that initiative. >> Well if I gave you the full road map they'd take me out of here with a hook out of this chair. >> You're too good for that, damn, almost got it from you. >> But we did announce the industries first commercial universal quantum computing project. A few weeks ago. It's called IBM Q, so we had some clever branding help, because Q makes me think of the personality in the James Bond movie who was always involved in the latest R&D research activity. And it really is the culmination of decades of research between IBM researchers and researchers around the world, to create this system that hopefully can solve problems to date, that are unsolvable today with classical computers. So, problems in areas like material science and chemistry. Last year we had announced quantum experience, which is an online access to a quantum capabilities in our Yorktown research laboratory. And over the last year, we've had more than 40,000 users access this capability. And they've actually executed a tremendous number of experiments. So we've learned from that, and now we're on this next leg of the journey. And we see a world where IBM Q could work together with our classical computers to solve really really tough problems. >> And that computing is driving a lot of the IOT, whether that's health care, to industrial, and everything in between. >> Well we're in the early stages of quantum, to be fair, but there's a lot of unique problems that we believe that it will solve. We do not believe that everything, of course, will move from classical to quantum. It will be a combination, an evolution, of the capabilities working together. But it's a very different system and it will have unique properties that allow us to do things differently. >> So, what are the basics? Why quantum computing? I presume it's performance, scale, cost, but it's not traditional, binary, computing, is that right? >> Yes. It's very, very different. In fact, if. >> Oh we just got the two minute sign. >> It's a very different computing model. It's a very different physical, computing model, right? It's built on this unit called a Q bit, and the interesting thing about a Q bit is it could be both a zero and a one at the same time. So it kind of twists our minds a little bit. But because of that, and those properties, it can solve very unique problems. But we're at the early part of the journey. So this year, our goal is to work with some organizations, learn from the commercialization of some of the first systems, which will be run in a cloud hosted model. And then we'll go from there. But, it's very promising. >> In the timeframe for commercial systems, have you guys released that? >> Well, this year, we'll start the commercial journey, but within the next few years we do plan to have a quantum computer that would then, basically, out strip the power of the largest super computers that we have today in the industry. But that's, you know, over the next few years we'll be evolving to that level. Because eventually, that's the goal, right? Is to solve the problems that we can't solve with today's classical computers. >> Talk about real quickly, in the last couple minutes, Blockchain, and where that's going, because you have a lot of banks and financial institutions looking at this as part of the messaging and the announcements here. >> Well, Blockchain is one of those workloads of course that we're optimizing with a lot that security work that I talked about earlier so. The target of our high security Blockchain services is LinuxONE, is driving a lot of encryption strategy. This week, in fact, we've seen a number of examples of Blockchain. One was talked about this morning, which was around diamond provenance, from the Everledger organization. Very clever implementation of Blockchain. We've had a number of financial institutions that are using Blockchain. And I also showed an interesting example today. Plastic Bank, which is an organization that's using Blockchain to allow ecosystem improvement, or improving our planet, if you will, by allowing communities to exchange plastic, recyclable plastic for currency. So it's really about enabling plastic to be turned into currency through the use of Blockchain. So a very novel example of a foundational research organization improving the environment and allowing communities to take advantage of that. >> Jamie thanks for stopping by the Cube, really appreciate giving the update and insight into the quantum, the Q project, and all the greatness around, all the hard work going to into the hybrid cloud, the security-osity is super important, thanks for sharing. >> It's good to see you. >> Okay we're live here, in Mandalay Bay, for IBM InterConnect 2017, stay with us for more live coverage, after this short break.